Robotics-Assisted Needle Steering for Percutaneous Interventions: Modeling and Experiments

Needle insertion and guidance plays an important role in medical procedures such as brachytherapy and biopsy. Flexible needles have the potential to facilitate precise targeting and avoid collisions during medical interventions while reducing trauma to the patient and post-puncture issues. Nevertheless, error introduced during guidance degrades the effectiveness of the planned therapy or diagnosis. Although steering using flexible bevel-tip needles provides great mobility and dexterity, a major barrier is the complexity of needle-tissue interaction that does not lend itself to intuitive control. To overcome this problem, a robotic system can be employed to perform trajectory planning and tracking by manipulation of the needle base. This research project focuses on a control-theoretic approach and draws on the rich literature from control and systems theory to model needle-tissue interaction and needle flexion and then design a robotics-based strategy for needle insertion/steering. The resulting solutions will directly benefit a wide range of needle-based interventions. The outcome of this computer-assisted approach will not only enable us to perform efficient preoperative trajectory planning, but will also provide more insight into needle-tissue interaction that will be helpful in developing advanced intraoperative algorithms for needle steering. Experimental validation of the proposed methodologies was carried out on a state of-the-art 5-DOF robotic system designed and constructed in-house primarily for prostate brachytherapy. The system is equipped with a Nano43 6-DOF force/torque sensor (ATI Industrial Automation) to measure forces and torques acting on the needle shaft. In our setup, an Aurora electromagnetic tracker (Northern Digital Inc.) is the sensing device used for measuring needle deflection. A multi-threaded application for control, sensor readings, data logging and communication over the ethernet was developed using Microsoft Visual C 2005, MATLAB 2007 and the QuaRC Toolbox (Quanser Inc.). Various artificial phantoms were developed so as to create a realistic medium in terms of elasticity and insertion force ranges; however, they simulated a uniform environment without exhibiting complexities of organic tissues. Experiments were also conducted on beef liver and fresh chicken breast, beef, and ham, to investigate the behavior of a variety biological tissues

A mechanics-based model for simulation and control of flexible needle insertion in soft tissue

AbstractIn needle-based medical procedures, beveled-tip exible needles are steered inside soft tissue with the aim of reaching pre-dened target locations. The efciency of needle-based interventions depends on accurate control of the needle tip. This paper presents a comprehensive mechanics-based model for simulation of planar needle insertion in soft tissue. The proposed model for needle deection is based on beam theory, works in real-time, and accepts the insertion velocity as an input that can later be used as a control command for needle steering. The model takes into account the effects of tissue deformation, needle-tissue friction, tissue cutting force, and needle bevel angle on needle deection. Using a robot that inserts a exible needle into a phantom tissue, various experiments are conducted to separately identify different subsets of the model parameters. The validity of the proposed model is veried by comparing the simulation results to the empirical data. The results demonstrate the accuracy of the proposed model in predicting the needle tip deection for different insertion velocities. I

Realistic tool-tissue interaction models for surgical simulation and planning

Surgical simulators present a safe and potentially effective method for surgical training, and can also be used in pre- and intra-operative surgical planning. Realistic modeling of medical interventions involving tool-tissue interactions has been considered to be a key requirement in the development of high-fidelity simulators and planners. The soft-tissue constitutive laws, organ geometry and boundary conditions imposed by the connective tissues surrounding the organ, and the shape of the surgical tool interacting with the organ are some of the factors that govern the accuracy of medical intervention planning.\ud \ud This thesis is divided into three parts. First, we compare the accuracy of linear and nonlinear constitutive laws for tissue. An important consequence of nonlinear models is the Poynting effect, in which shearing of tissue results in normal force; this effect is not seen in a linear elastic model. The magnitude of the normal force for myocardial tissue is shown to be larger than the human contact force discrimination threshold. Further, in order to investigate and quantify the role of the Poynting effect on material discrimination, we perform a multidimensional scaling study. Second, we consider the effects of organ geometry and boundary constraints in needle path planning. Using medical images and tissue mechanical properties, we develop a model of the prostate and surrounding organs. We show that, for needle procedures such as biopsy or brachytherapy, organ geometry and boundary constraints have more impact on target motion than tissue material parameters. Finally, we investigate the effects surgical tool shape on the accuracy of medical intervention planning. We consider the specific case of robotic needle steering, in which asymmetry of a bevel-tip needle results in the needle naturally bending when it is inserted into soft tissue. We present an analytical and finite element (FE) model for the loads developed at the bevel tip during needle-tissue interaction. The analytical model explains trends observed in the experiments. We incorporated physical parameters (rupture toughness and nonlinear material elasticity) into the FE model that included both contact and cohesive zone models to simulate tissue cleavage. The model shows that the tip forces are sensitive to the rupture toughness. In order to model the mechanics of deflection of the needle, we use an energy-based formulation that incorporates tissue-specific parameters such as rupture toughness, nonlinear material elasticity, and interaction stiffness, and needle geometric and material properties. Simulation results follow similar trends (deflection and radius of curvature) to those observed in macroscopic experimental studies of a robot-driven needle interacting with gels

Energy shaping control for robotic needle insertion

This work investigates the use of energy shaping control to reduce deflection in slender beams with tip load and actuation at the base. The ultimate goal of this research is a buckling avoidance strategy for robotic-assisted needle insertion. To this end, the rigid-link model of a flexible beam actuated at the base and subject to tip load is proposed, and an energy shaping approach is employed to construct a nonlinear controller that accounts for external forces. A comparative simulation study highlights the benefits of the proposed approach over a linear control baseline and a simplified nonlinear control

A Novel Flexible and Steerable Probe for Minimally Invasive Soft Tissue Intervention

Current trends in surgical intervention favour a minimally invasive (MI) approach, in which complex procedures are performed through increasingly small incisions. Specifically, in neurosurgery, there is a need for minimally invasive keyhole access, which conflicts with the lack of maneuverability of conventional rigid instruments. In an attempt to address this fundamental shortcoming, this thesis describes the concept design, implementation and experimental validation of a novel flexible and steerable probe, named “STING” (Soft Tissue Intervention and Neurosurgical Guide), which is able to steer along curvilinear trajectories within a compliant medium. The underlying mechanism of motion of the flexible probe, based on the reciprocal movement of interlocked probe segments, is biologically inspired and was designed around the unique features of the ovipositor of certain parasitic wasps. Such insects are able to lay eggs by penetrating different kinds of “host” (e.g. wood, larva) with a very thin and flexible multi-part channel, thanks to a micro-toothed surface topography, coupled with a reciprocating “push and pull” motion of each segment. This thesis starts by exploring these foundations, where the “microtexturing” of the surface of a rigid probe prototype is shown to facilitate probe insertion into soft tissue (porcine brain), while gaining tissue purchase when the probe is tensioned outwards. Based on these findings, forward motion into soft tissue via a reciprocating mechanism is then demonstrated through a focused set of experimental trials in gelatine and agar gel. A flexible probe prototype (10 mm diameter), composed of four interconnected segments, is then presented and shown to be able to steer in a brain-like material along multiple curvilinear trajectories on a plane. The geometry and certain key features of the probe are optimised through finite element models, and a suitable actuation strategy is proposed, where the approach vector of the tip is found to be a function of the offset between interlocked segments. This concept of a “programmable bevel”, which enables the steering angle to be chosen with virtually infinite resolution, represents a world-first in percutaneous soft tissue surgery. The thesis concludes with a description of the integration and validation of a fully functional prototype within a larger neurosurgical robotic suite (EU FP7 ROBOCAST), which is followed by a summary of the corresponding implications for future work

Modeling and simulation of an active robotic device for flexible needle insertion

Master'sMASTER OF ENGINEERIN

Mechanical characterisation and FEM modelling of biological deformation for surgical simulation

This thesis sought to explore the use of minimally invasive surgery via biomechanical simulation of soft tissue deformation and needle path planning insertion. When surgeons are placed under mechanical stress, human brain cells exhibit the viscoelastic behaviour of solid structures. However, the behavioural mechanisms of tissues/cells are not yet fully understood, and more information is needed to reliably calculate tissue/cell deformation. The research objectives and methodologies were: First, to objectively investigate and characterise the mechanical properties of biological tissues/cells by using experimental atomic force microscopy (AFM) data (see CHAPTER 3). This method was used to analyse the cell's mechanical behaviours with a developed numerical algorithm. The difference between two human brain cells (normal HNC-2 and U87 cancer cells) was studied to determine their mechanical properties so that these could then be applied to our proposed 3D model (see CHAPTER 5). Second, using the measured experimental AFM data, a system identification of AFM characterisation was implemented in another chapter (CHAPTER 4), which for comparison, was based on a MATLAB algorithm. The results showed that the model that was identified for AFM matched the measured experimental AFM data. Third, to establish a finite element method (FEM) for real-time modelling of nonlinear soft tissue deformation behaviours using a three-dimensional (3D) dynamic nonlinear FEM; this method was developed to establish the large-range deformation of tissue/cells with second- order Piola-Kirchhoff stress (CHAPTER 5). A Newmark numerical process was implemented to solve the partial differential equations (PDEs) that resulted from the FEM. Experimental analysis of biological human brain cells was conducted to verify and validate the nonlinear FEM for simulating deformation. Fourth, to establish a method for real-time motion plan modelling of nonlinear needle deflection during needle insertion using the third objective to implement the nonlinear FEM for needle path planning. Last, to use an application of bio-heat transfer of potential needle tip path planning by applying a bioheat transfer-based method (CHAPTER 6); this method was established for optimal path planning for needle insertion in the presence of soft tissue deformation. A bio- heat transfer was used to develop a temperature distribution for path planning to reach the target and avoid obstacles in cubic, liver and brain cell models. The algorithm defines the optimal path for needle tip placement; the needle tip placement is determined by the temperature distribution, which in turn, is based on soft tissue deformation that occurs in the process of needle insertion. When force was applied during the needle penetration process, the deflection accrued was based on the geometry of nonlinear material. Based on our simulation of 3D FEM discretisation of the Pennes' Bio-heat Transfer Equation, the distribution of the temperature from single point temperature sources was performed to determine the degree of transient thermal. Furthermore, the distribution was used to model thermal stresses and strains within the cell/tissue, which result from the heat source. The main contribution to this field is building a new conceptual design methodology for characterisation of the mechanical properties of biological cells by extracts of the mechanical properties of two biological human brain cells (normal HNC-2 and cancer U87 MG cells), and the experimental use of AFM for the first time. Also, linear FEM for soft tissue/needle insertion with large deformation is developed and adapted to our three-dimensional dynamic FEM soft tissue/cell modelling using numerical integration methods. Verification of the experimental work and the proposed method is examined mathematically and systematically using a system identification schema. Moreover, bio-heat transfer for needle insertion is implemented based on the proposed FEM soft tissue deformation modelling to represent path planning. The investigation of needle insertion into soft tissue/cell deformation using bioheat transfer FEM has not been done before

imaged-based tip force estimation on steerable intracardiac catheters using learning-based methods

Minimally invasive surgery has turned into the most commonly used approach to treat cardiovascular diseases during the surgical procedure; it is hypothesized that the absence of haptic (tactile) feedback and force presented to surgeons is a restricting factor. The use of ablation catheters with the integrated sensor at the tip results in high cost and noise complications. In this thesis, two sensor-less methods are proposed to estimate the force at the intracardiac catheter’s tip. Force estimation at the catheter tip is of great importance because insufficient force in ablation treatment may result in incomplete treatment and excessive force leads to damaging the heart chamber. Besides, adding the sensor to intracardiac catheters adds complexity to their structures. This thesis is categorized into two sensor-less approaches: 1- Learning-Based Force Estimation for Intracardiac Ablation Catheters, 2- A Deep-Learning Force Estimator System for Intracardiac Catheters. The first proposed method estimates catheter-tissue contact force by learning the deflected shape of the catheter tip section image. A regression model is developed based on predictor variables of tip curvature coefficients and knob actuation. The learning-based approach achieved force predictions in close agreement with experimental contact force measurements. The second approach proposes a deep learning method to estimate the contact forces directly from the catheter’s image tip. A convolutional neural network extracts the catheter’s deflection through input images and translates them into the corresponding forces. The ResNet graph was implemented as the architecture of the proposed model to perform a regression. The model can estimate catheter-tissue contact force based on the input images without utilizing any feature extraction or pre-processing. Thus, it can estimate the force value regardless of the tip displacement and deflection shape. The evaluation results show that the proposed method can elicit a robust model from the specified data set and approximate the force with appropriate accuracy